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One of the common pain points that we have come across in big
organisations is the last-mile delivery of data science applications.
One common delivery vehicle is to create dashboards (BI). But the one, that’s very useful and neglected more often than not, is to create APIs and provide seamless integration with other applications within the company. This requires you to have a basic understanding of machine learning, server-side programming and front-end application.

In this workshop, you would learn how to build a seamless end-to-end data driven application - Data Exploration, Machine Learning Model, RESTful API and Web Application - to solve a business prediction problem.

Audience

A programmer but not a data science practioner:
A programmer with experience in server-side or front-end development
and maybe has some familiarity with doing data analysis. You could be looking to transition in to building data driven products or a create a richer product experience with data.

A data science practioner but not a programmer:
A data science with some experience in doing data analysis, preferably in a scripting language (R/Python/Scala), but wants to get a deeper and a more applied perspective on creating data driven products.

Pre-requisites

Participants should be comfortable with Python programming language and have prior experience with using Python for Data Science.

Course Outline

Session 1: Introduction and Concepts

Approach for building ML products - the process - Problem definition and dataset

Session 11: Repeatable ML as a Service

Session 12: Practice Session & Wrap-up

Best practices in building ML service

Challenges in managing ML in production

Where to go from here

Testimonials

The 3 day Machine Learning Course is the perfect session to attend if
you are interested in the field but are still a novice/beginner. Anand
and Amit make a great team and broke down seemingly complex concepts
into simple concepts that was easy to understand for everyone in the
room. The course was really dynamic, in the sense the trainers used
input given to them by the trainees, ran into roadblocks on the way, and
brainstormed with us to come up with means to arrive at a solution. I
feel like this is a better method than the usual way of using an
existing problem and being aware of its potential pitfalls beforehand. I
would highly recommend it to anyone else who wants to learn Machine
Learning and has 3 days to spare.

Infrastructure

Trainers

Amit Kapoor teaches the craft of telling visual stories with data. He conducts workshops and trainings on Data Science in Python and R, as well as on Data Visualisation topics. His background is in strategy consulting having worked with AT Kearney in India, then with Booz & Company in Europe and more recently for startups in Bangalore. He did his B.Tech in Mechanical Engineering from IIT, Delhi and PGDM (MBA) from IIM, Ahmedabad.

Anand has been crafting beautiful software since a decade and half. He’s now building a data science platform, rorodata, which he recently co-founded. He regularly conducts advanced programming courses through Pipal Academy.
He is co-author of web.py, a micro web framework in Python. He has worked at Strand Life Sciences and Internet Archive.

Bargava is a practicing Data Scientist. He has 14 years of experience delivering business analytics solutions to Investment Banks, Entertainment Studios and High-Tech companies. He has given talks and conducted workshops on Data Science, Machine Learning, Deep Learning and Optimization in Python and R. He has a Masters in Statistics from University of Maryland, College Park, USA. He is an ardent NBA fan.